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Semester 1

Modelling Ecosystem Processes (ECSC10040)

Subject

Ecological Science

College

SCE

Credits

20

Normal Year Taken

4

Delivery Session Year

2022/2023

Pre-requisites

Course Summary

This course introduces the key approaches to modelling in the ecological and environmental sciences. It will provide students with the conceptual understanding of how to represent complex ecosystems within a model framework and practical experience of model design, construction, sensitivity analysis, calibration, validation/evaluation and analysis to address specific objectives. The students will learn how models can provide real-world, actionable evidence for sustainable ecosystem interventions against a backdrop of global environmental change. The students will learn how models can help test and generate new hypotheses to understand ecosystem functions. Students are challenged to formulate testable hypotheses, and design suitable models of differing complexity, including both discrete (i.e. agent-based) and continuous (i.e. pool-based), approaches. They will use existing real-world applicable terrestrial ecosystem models (e.g. DALEC and MORPH) to test student-created hypotheses of ecosystem function in response to external factors (i.e. climate and disturbance).The course will consist of 10 three-hour sessions. These sessions include lectures and practical exercises that offer hands-on experience and reinforce the concepts introduced in the lectures.

Course Description

Computer-based models are widely used in many areas of science, especially in the ecological and environmental sciences. The emphasis of this course will be on the application and development of models within ecosystems.The course consists of two components:1) Weekly one-hour lectures provide conceptual information on models and the stages in their creation, calibration, evaluation and use. Examples provided will be derived from published datasets giving context to each analysis. The lectures provide the foundation for practical exercises.The lectures will cover the following topics: Model approaches and potential uses in ecological and environmental sciences Model validation/evaluation Introduction to agent-based modelling (ABM) Model design and building using ABMs Calibration approaches Forecasting time-series data Sensitivity analysis Using models to address global challenges2) Weekly two-hour computer practical sessions will provide experience to reinforce the concepts introduced in the lectures. These sessions will involve individual and group-working opportunities. Sessions will be based on published models and datasets, where possible, to place the activities in context. Each practical session will have a tangible outcome based on a realistic scenario, which can feed into decision-making processes.The practical sessions will cover:Week 1: Simple model design - from hypotheses to graphical representationWeek 2: Model evaluation T-Rex scavenger case studyWeek 3: ABM 1 Using NetLogo (predator/prey model)Week 4: ABM 2 Model design and buildingWeek 5: Reading data into R. Calibrating linear and non-linear models using least squares approachWeek 6: Time-series Forecasting climatic changesWeek 7: Building pool-based models. Investigating model sensitivityWeek 8: Diagnosing the impact of future climate on steady-state and regrowth dynamics in forest ecosystemsWeek 9: Supported project developmentWeek 10: Supported project development

Assessment Information

Written Exam 0%, Coursework 100%, Practical Exam 0%

Additional Assessment Information

Coursework: 100%«br /»«br /»Coursework will consist of four assignments:«br /»«br /»1. Design and create a graphical representation of a simple model (Learning outcomes 1 and 2; formative)«br /»«br /»2. Agent-based modelling - simple model design and build (Learning outcomes 1 and 2; 20%)«br /»«br /»3. Short assignment - model calibration, evaluation and validation (Learning outcomes 3 and 4; 30%)«br /»«br /»4. Long modelling assignment project on individual topic chosen by the student (Learning outcomes 2, 3 and 4; 50%)«br /»

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